With the widespread adoption of deep learning, reinforcement learning (RL) has
experienced a dramatic increase in popularity, scaling to previously intractable problems …

Deep reinforcement learning has achieved great success in many fields and has shown
promise in learning robust skills for robot control in recent years. However, sampling …

Sample efficiency and exploration remain major challenges in online reinforcement learning
(RL). A powerful approach that can be applied to address these issues is the inclusion of …

A compelling use case of offline reinforcement learning (RL) is to obtain a policy initialization
from existing datasets followed by fast online fine-tuning with limited interaction. However …

Recent work has shown that offline reinforcement learning (RL) can be formulated as a
sequence modeling problem (Chen et al., 2021; Janner et al., 2021) and solved via …

Offline reinforcement learning (RL) defines the task of learning from a static logged dataset
without continually interacting with the environment. The distribution shift between the …

Offline Reinforcement Learning (RL) aims to learn policies from previously collected
datasets without exploring the environment. Directly applying off-policy algorithms to offline …

We consider a hybrid reinforcement learning setting (Hybrid RL), in which an agent has
access to an offline dataset and the ability to collect experience via real-world online …

Placement is a critical step in modern chip design, aiming to determine the positions of
circuit modules on the chip canvas. Recent works have shown that reinforcement learning …

Progress in deep learning highlights the tremendous potential of utilizing diverse robotic
datasets for attaining effective generalization and makes it enticing to consider leveraging …